Model Explainer

Feature Importances

Feature Importances

Model performance metrics

metric Score
accuracy 0.667
precision 0.223
recall 0.511
f1 0.311
roc_auc_score 0.652
pr_auc_score 0.239
log_loss 0.693

Confusion Matrix

How many false positives and false negatives?

Precision Plot

Does fraction positive increase with predicted probability?

Classification Plot

Distribution of labels above and below cutoff

ROC AUC Plot

Trade-off between False positives and false negatives

PR AUC Plot

Trade-off between Precision and Recall

Lift Curve

Performance how much better than random?

Cumulative Precision

Expected distribution for highest scores

Individual Predictions

Select Random Index

Selected index: 49798

Prediction

label probability
0* 50.0 %
1 50.0 %

Contributions Plot

How has each feature contributed to the prediction?

Partial Dependence Plot

Contributions Table

How has each feature contributed to the prediction?
Reason Effect
Average of population 50.0%
is_referral_1 = 0.0 +0.0%
recency_2 = 1.0 +0.0%
history = 0.2816685140132904 +0.0%
offer_No Offer = 0.0 +0.0%
used_bogo_0 = 0.0 +0.0%
channel_Web = 0.0 -0.0%
channel_Phone = 0.0 +0.0%
offer_Discount = 0.0 -0.0%
zip_code_Urban = 0.0 +0.0%
zip_code_Rural = 0.0 -0.0%
recency_1 = 0.0 -0.0%
recency_10 = 0.0 +0.0%
recency_11 = 0.0 +0.0%
recency_9 = 0.0 +0.0%
recency_7 = 0.0 +0.0%
zip_code_Surburban = 1.0 -0.0%
recency_3 = 0.0 -0.0%
used_discount_1 = 0.0 -0.0%
offer_Buy One Get One = 1.0 +0.0%
recency_12 = 0.0 +0.0%
recency_6 = 0.0 +0.0%
recency_8 = 0.0 +0.0%
recency_4 = 0.0 +0.0%
channel_Multichannel = 1.0 -0.0%
recency_5 = 0.0 +0.0%
Other features combined +0.0%
Final prediction 50.0%

What if...

Select Random Index

Selected index: 3442

Prediction

label probability
0 50.0 %
1 50.0 %

Feature Input

Adjust the feature values to change the prediction

Contributions Plot

How has each feature contributed to the prediction?

Partial Dependence Plot

Contributions Table

How has each feature contributed to the prediction?
Reason Effect
Average of population 50.0%
is_referral_1 = 0.0 +0.0%
history = 0.05069262534379959 +0.0%
offer_No Offer = 0.0 +0.0%
channel_Phone = 0.0 +0.0%
used_bogo_0 = 0.0 +0.0%
offer_Discount = 0.0 -0.0%
channel_Web = 1.0 +0.0%
recency_1 = 0.0 -0.0%
zip_code_Rural = 0.0 -0.0%
recency_10 = 0.0 +0.0%
recency_2 = 0.0 -0.0%
recency_9 = 0.0 +0.0%
zip_code_Urban = 0.0 +0.0%
recency_11 = 0.0 +0.0%
recency_3 = 0.0 -0.0%
used_discount_1 = 0.0 +0.0%
offer_Buy One Get One = 1.0 +0.0%
recency_7 = 0.0 +0.0%
recency_12 = 0.0 +0.0%
zip_code_Surburban = 1.0 -0.0%
recency_8 = 0.0 +0.0%
recency_6 = 0.0 +0.0%
recency_4 = 0.0 +0.0%
recency_5 = 1.0 -0.0%
channel_Multichannel = 0.0 -0.0%
Other features combined +0.0%
Final prediction 50.0%

Feature Dependence

Shap Summary

Ordering features by shap value

Shap Dependence

Relationship between feature value and SHAP value